It is difficult to diagnose and accommodate the faults if disturbances and faults exist simultaneously in the controlled plants. In this paper, an anti-disturbance fault tolerant control (FTC) scheme is presented for a class of nonlinear systems with both faults and multiple disturbances. The multiple disturbances are supposed to include two types including the uncertain modeled disturbances and norm bounded uncertain disturbances. A composite fault tolerant controller is constructed by integrating a fault accommodation from diagnosis observer with additional disturbance rejection and attenuation performance for two different types of disturbances. As a result, the fault can be accommodated and the multiple disturbances can be rejected and attenuated simultaneously. Simulations for a flight control system are given to show the efficiency of the proposed approach.

No abstract

No abstract

Short-term traffic flow prediction is important to realize real-time traffic instruction. However, due to the existing strong nonlinearity and non-stationarity in short-term traffic volume data, it is hard to obtain a satisfactory result through the traditional method. To this end, this paper develops an innovative hybrid method based on the time varying filtering based empirical mode decomposition (TVF-EMD) and least square support vector machine (LSSVM). Specifically, TVF-EMD is firstly used to deal with the implied non-stationarity in the original data by decomposing them into several different subseries. Then, the LSSVM models are established for each subseries to capture the linear and nonlinear characteristics embedded in the original data, and the corresponding prediction results are superimposed to obtain the final one. Finally, case studies based on two groups of data measured from an arterial road intersection are employed to evaluate the performance of the proposed method. The experimental results indicate it outperforms the other involved models. For example, compared with the LSSVM model, the average improvements by the proposed method in terms of the indexes of mean absolute error, mean relative percentage error, root mean square error and root mean square relative error are 7.397, 15.832%, 10.707 and 24.471%, respectively.

No abstract